Download Algorithmic Probability and Friends. Bayesian Prediction and by David L. Dowe (auth.), David L. Dowe (eds.) PDF
By David L. Dowe (auth.), David L. Dowe (eds.)
Algorithmic chance and pals: court cases of the Ray Solomonoff eighty fifth memorial convention is a set of unique paintings and surveys. The Solomonoff eighty fifth memorial convention was once held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff (1926-2009), honouring his a number of pioneering works - such a lot relatively, his innovative perception within the early Nineteen Sixties that the universality of common Turing Machines (UTMs) should be used for common Bayesian prediction and synthetic intelligence (machine learning). This paintings maintains to more and more impact and under-pin facts, econometrics, desktop studying, facts mining, inductive inference, seek algorithms, information compression, theories of (general) intelligence and philosophy of technological know-how - and purposes of those components. Ray not just anticipated this because the route to real synthetic intelligence, but additionally, nonetheless within the Nineteen Sixties, expected phases of development in computer intelligence which might eventually result in machines surpassing human intelligence. Ray warned of the necessity to expect and speak about the aptitude outcomes - and hazards - faster instead of later. potentially foremostly, Ray Solomonoff used to be an excellent, chuffed, frugal and adventurous person of mild unravel who controlled to fund himself whereas electing to behavior loads of his paradigm-changing study outdoors of the collage method. the amount comprises 35 papers relating the abovementioned issues in tribute to Ray Solomonoff and his legacy.
Read or Download Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011 PDF
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Additional resources for Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers from the Ray Solomonoff 85th Memorial Conference, Melbourne, VIC, Australia, November 30 – December 2, 2011
L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp. 37–52. Springer, Heidelberg (2013) 115. : An exact method for the computation of the connectivity of random nets. Bulletin of Mathematical Biophysics 14(2), 153–157 (1952) 116. : An optically driven airborne chopper. In: Proceedings of the 3rd Typhoon Symposium, p. 205 (1953) 117. : Eﬀects of Heisenberg’s principle on channel capacity. E. 43, 484 (April 1955) 118. : An inductive inference machine. Dartmouth Summer Research Project on Artiﬁcial Intelligence, A privately circulated report (August 1956) 119.
Message length as an eﬀective Ockham’s razor in decision tree induction. In: Proc. 8th Int. Workshop on Artif. Intelligence and Statistics (AI+STATS 2001), pp. 253–260 (January 2001) ¨ 92. : Diverse consequences of algorithmic probability. L. ) Solomonoﬀ Festschrift. LNCS (LNAI), vol. 7070, pp. 285–298. Springer, Heidelberg (2013) 93. : A general theory of prediction. , Privately circulated report (1963) 94. : Autonomous theory building systems. Neural Networks and Adaptive Learning, Schloss Reisenberg, Knowledge Processing and its Applications Series (1990) 95.
Like at least (many or) most of these, [163, sec. 4 (Diversity)] concerns using a weighted mixture of theories - rather than a single theory - for prediction. The completeness and statistical consistency (and other merits) of approaches to inference and prediction based on algorithmic information theory have been discussed here a few times, but “There is, however another aspect of algorithmic probability that people ﬁnd disturbing - it would seem to take too much time and memory to ﬁnd a good prediction.